Équira

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Inspiration

The traditional academic equivalency process is slow, manual, and inconsistent. Students transferring between universities or across countries often lose credits due to delays and subjective evaluation systems. Institutions spend weeks reviewing syllabi manually, creating administrative bottlenecks and financial loss for students.

Equira was inspired by the need to automate and standardize this process using semantic AI, transforming equivalency evaluation from a human-dependent workflow into a fast, data-driven system.

What it does

Equira is an AI-powered academic equivalency engine that compares course syllabi using semantic analysis.

It:

Accepts uploaded course syllabi

Extracts structured content using NLP

Computes semantic similarity using a fine-tuned language model

Generates equivalency scores and structured reports

The system reduces evaluation time from weeks to seconds while maintaining high consistency.

How we built it

We built Equira using:

A curated dataset of 10,000+ labeled course equivalency mappings

A fine-tuned BERT-based model adapted to academic content

Python backend for model inference

React-based frontend for uploading and viewing results

Secure hosting for demonstration and testing

The model was trained on labeled equivalency pairs and evaluated using train/test splits to measure generalization performance.

Challenges we ran into

Cleaning and structuring academic syllabus data from inconsistent formats

Creating labeled equivalency pairs with clear validation criteria

Ensuring the model generalizes across different universities

Avoiding overfitting during fine-tuning

Building a functional MVP while managing limited infrastructure

Accomplishments that we’re proud of

Successfully building a working MVP interface

Fine-tuning a domain-specific semantic model

Achieving strong performance on labeled evaluation data

Structuring a scalable architecture for future institutional deployment

Filing intellectual property related to the equivalency evaluation framework

What we learned

Academic data is messy and requires significant preprocessing

Domain adaptation is critical for meaningful semantic matching

Clear labeling standards improve model performance significantly

Early prototyping accelerates clarity and iteration

Validation metrics must be carefully interpreted to avoid overconfidence

What’s next for Equira

Expand the labeled dataset for broader coverage

Improve generalization across international universities

Deploy a production-ready SaaS infrastructure

Onboard pilot institutions

Develop institutional dashboard and API integrations

Transition from prototype to revenue-generating platform

Built With

• ai